Tackling background ambiguities in multi-class few-shot point cloud semantic segmentation
- Lai, Lvlong
- Chen, Jian
- Zhang, Chi
- Zhang, Zehong
- Lin, Guosheng
- Wu, Qingyao
DOIAbstract
Few-shot point cloud semantic segmentation learns to segment novel classes with scarce labeled samples. Within an episode, a novel target class is defined by a few support samples with corresponding binary masks, where only the points of this class are labeled as foreground and others are regarded as background. In the tasks involving multiple target classes, since the meanings of background are diverse for different target classes, background ambiguities appear: Some points labeled as background in one support sample may be of other target classes. It will result in incorrect guidance and damage model's segmentation performance. However, previous methods in the literature do not consider this problem. In this paper, we propose a simple yet...
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